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Multi-source domain adversarial graph convolutional networks for rolling mill health states diagnosis under variable working conditions

Author(s): ORCID (School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang, China)
(School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang, China)
(School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang, China)
(School of Education, Nanchang Institute of Science & Technology, Nanchang, China)
(Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217231225986
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/14759217231225986.
  • About this
    data sheet
  • Reference-ID
    10775650
  • Published on:
    29/04/2024
  • Last updated on:
    29/04/2024
 
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